MetaFlowX

pipelinePNG

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Reads Quality

  Reads Quality Statistics Table

Assembly Contigs

  Length distribution of all contigs from all samples

  GC distribution of all contigs from all samples

  Statistics of contig from each specimen and their average length.

Gene Set

  Length distribution of non-redundant gene sets.

  The distribution of the total number of genes and the average gene length in each sample.

  PCA visualization of gene abundance profile.

  Distribution of COG functional databases' level 1 category of non-redundant gene sets.

  Distribution of GO functional databases' level 1 category of non-redundant gene sets.

  Distribution of KEGG functional databases' level 1 category of non-redundant gene sets.

  Distribution of CAZY functional databases' level 1 category of non-redundant gene sets.

Binning

  A table containing information on bin quality, genome size, species annotation.

  The scatter plot illustrates the level of completeness and contamination for each bin, with the color green indicating high quality.

  The scatter plot displays the number of contigs and the total length of contigs for each bin. The size of the bubble is equal to the total length of contigs divided by the number of contigs.

  Treemap chart of the overall taxonomic hierarchical classifications distribution of all bins, based on GTDB annotation.

  Distribution of KEGG functional databases' level 1 category of each bin.

  Distribution of CAzY functional databases' level 1 category of each bin.

  Distribution of COG functional databases' level 1 category of each bin.

  Distribution of GO functional databases' level 1 category of each bin.

  Multi PCA plot showing the abundance of bins calculated using multiple CoverM methods.

Taxonomy Classification | MetaPhlAn

  Top 20 taxonomic distributions of each sample based on relative abundance, with all other species classified as 'Others'.

  Sunburst chart of the overall average distribution of seven different species hierarchical classifications.

  PCA visualization of MetaPhlAn abundance profile.

Metabolic Analysis Network | HUMAnN

  The heat map displays the relative abundance of MetaCyc pathways, with data related to "UNMAPPED" & "UNINTEGRATED" not being shown.

References

[1] Blin, K. et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Research 51, W46-W50 (2023). https://doi.org:10.1093/nar/gkad344
[2] Pascal Andreu, V. et al. BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes. mSystems 6, e0093721 (2021). https://doi.org:10.1128/mSystems.00937-21
[3] Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nature Methods 11, 1144-1146 (2014). https://doi.org:10.1038/nmeth.3103
[4] Hickl, O., Queirós, P., Wilmes, P., May, P. & Heintz-Buschart, A. binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets. Brief Bioinform 23 (2022). https://doi.org:10.1093/bib/bbac431
[5] Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research 51, D690-D699 (2023). https://doi.org:10.1093/nar/gkac920
[6] Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150-3152 (2012). https://doi.org:10.1093/bioinformatics/bts565
[7] Chklovski, A., Parks, D., Woodcroft, B. & Tyson, G. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. (2022).
[8] Pan, S., Zhu, C., Zhao, X.-M. & Coelho, L. P. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments. Nature Communications 13, 2326 (2022). https://doi.org:10.1038/s41467-022-29843-y
[9] Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal 11, 2864-2868 (2017). https://doi.org:10.1038/ismej.2017.126
[10] Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Molecular Biology and Evolution 38, 5825-5829 (2021). https://doi.org:10.1093/molbev/msab293
[11] Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology (2023). https://doi.org:10.1038/s41587-023-01688-w
[12] Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357-359 (2012). https://doi.org:10.1038/nmeth.1923
[13] Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925-1927 (2020). https://doi.org:10.1093/bioinformatics/btz848
[14] Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biology 20, 257 (2019). https://doi.org:10.1186/s13059-019-1891-0
[15] Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 10 (2021). https://doi.org:10.7554/eLife.65088
[16] Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605-607 (2016). https://doi.org:10.1093/bioinformatics/btv638
[17] Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3-11 (2016). https://doi.org:10.1016/j.ymeth.2016.02.020
[18] Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019). https://doi.org:10.7717/peerj.7359
[19] Wang, Z., Huang, P., You, R., Sun, F. & Zhu, S. MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities. Genome Biology 24, 1 (2023). https://doi.org:10.1186/s13059-022-02832-6
[20] Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). https://doi.org:10.1186/1471-2105-11-119
[21] Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology 3, 836-843 (2018). https://doi.org:10.1038/s41564-018-0171-1
[22] Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14, 417-419 (2017). https://doi.org:10.1038/nmeth.4197
[23] Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods 18, 366-368 (2021). https://doi.org:10.1038/s41592-021-01101-x
[24] Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014). https://doi.org:10.1093/bioinformatics/btu170
[25] Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021). https://doi.org:10.1093/gigascience/giab008
[26] Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes De Novo Assembler. Curr Protoc Bioinformatics 70, e102 (2020). https://doi.org:10.1002/cpbi.102
[27] Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Research 50, D912-D917 (2022). https://doi.org:10.1093/nar/gkab1107